We adopt the same generator as ESRGAN [1]} and Real-ESRGAN [2]}. Specifically, it comprises several residual-in-residual dense blocks (RRDB), as shown in Fig. REF . At the end of the network, a pixel-shuffle upsampling and two convolution layers are applied for reconstruction. For discriminator, we apply the same architecture as Real-ESRGAN, i.e., a U-Net design to better consider the realness values for each pixel.
Note that the restoration performance of Real-ESRGAN is the theoretical upper bound of our proposed MM-RealSR, as MM-RealSR needs to learn another objective for interactive modulation.
